Mở Bài
Trí tuệ nhân tạo (AI) đang cách mạng hóa ngành dịch vụ tài chính toàn cầu, từ phân tích rủi ro đến dịch vụ khách hàng tự động. Chủ đề “How Is Artificial Intelligence Being Used In Financial Services?” thường xuyên xuất hiện trong các đề thi IELTS Reading gần đây, đặc biệt từ Cambridge IELTS 15 trở đi, phản ánh xu hướng công nghệ đương đại.
Bài viết này cung cấp một bộ đề thi IELTS Reading hoàn chỉnh với ba passages từ dễ đến khó, giúp bạn:
- Làm quen với đề thi thật gồm 40 câu hỏi chuẩn quốc tế
- Rèn luyện 7 dạng câu hỏi phổ biến: Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, Summary Completion, Matching Features và Short-answer Questions
- Nắm vững từ vựng chuyên ngành công nghệ tài chính
- Áp dụng chiến lược làm bài hiệu quả với giải thích chi tiết từng câu
Bộ đề phù hợp cho học viên từ band 5.0 trở lên, muốn nâng cao kỹ năng đọc hiểu học thuật và chuẩn bị tốt nhất cho kỳ thi IELTS thực tế.
1. Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test là bài kiểm tra khả năng đọc hiểu tiếng Anh học thuật trong thời gian giới hạn. Bạn cần hoàn thành:
- Thời gian: 60 phút cho toàn bộ bài thi
- Số lượng: 3 passages với độ dài tăng dần
- Tổng số câu hỏi: 40 câu
- Phân bổ thời gian khuyến nghị:
- Passage 1 (Easy): 15-17 phút
- Passage 2 (Medium): 18-20 phút
- Passage 3 (Hard): 23-25 phút
Lưu ý quan trọng: Bạn phải tự chuyển đáp án vào Answer Sheet trong 60 phút. Không có thời gian bổ sung để chép đáp án như phần Listening.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi thường gặp nhất:
- Multiple Choice – Chọn đáp án đúng từ các phương án cho sẵn
- True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
- Yes/No/Not Given – Xác định quan điểm tác giả
- Matching Headings – Ghép tiêu đề phù hợp với đoạn văn
- Summary/Note Completion – Điền từ vào chỗ trống trong đoạn tóm tắt
- Matching Features – Ghép đặc điểm với đối tượng tương ứng
- Short-answer Questions – Trả lời ngắn gọn (không quá 3 từ)
Mỗi dạng câu hỏi yêu cầu kỹ năng đọc khác nhau: skimming (đọc lướt), scanning (đọc tìm kiếm), và reading for detail (đọc chi tiết).
2. IELTS Reading Practice Test
PASSAGE 1 – The Dawn of AI in Banking
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Artificial intelligence is transforming the banking sector in ways that were unimaginable just a decade ago. From chatbots that answer customer queries 24/7 to sophisticated algorithms that detect fraudulent transactions in real-time, AI has become an integral part of modern financial services. Banks around the world are investing heavily in this technology, recognizing its potential to improve efficiency, reduce costs, and enhance customer satisfaction.
One of the most visible applications of AI in banking is the use of virtual assistants and chatbots. These automated systems can handle thousands of customer inquiries simultaneously, providing instant responses to common questions about account balances, transaction history, and basic banking procedures. Unlike human operators, these AI-powered assistants never need breaks, holidays, or sleep. They can operate continuously, ensuring that customers receive support whenever they need it, regardless of time zones or public holidays.
Fraud detection represents another critical area where AI excels. Traditional fraud detection systems relied on rule-based approaches that could only identify known patterns of fraudulent behavior. However, modern AI systems use machine learning to analyze millions of transactions and identify suspicious patterns that humans might miss. These systems learn from every transaction, continuously improving their ability to distinguish between legitimate activities and potential fraud. When a potentially fraudulent transaction is detected, the system can instantly freeze the account or request additional verification from the customer.
Credit scoring has also been revolutionized by artificial intelligence. In the past, banks used relatively simple formulas based on limited data points such as income, employment history, and previous credit history. AI systems can now analyze hundreds or even thousands of variables, including unconventional data sources like social media activity, online shopping behavior, and mobile phone usage patterns. This comprehensive approach allows banks to make more accurate lending decisions, potentially extending credit to deserving customers who might have been rejected by traditional scoring methods.
AI is also improving investment advisory services. Robo-advisors use algorithms to create personalized investment portfolios based on individual risk tolerance, financial goals, and market conditions. These automated systems can rebalance portfolios automatically, ensuring that investments remain aligned with the customer’s objectives. While they cannot replace human financial advisors for complex situations, robo-advisors provide an affordable alternative for customers with straightforward investment needs.
In the realm of regulatory compliance, AI helps banks navigate the increasingly complex landscape of financial regulations. Banks must comply with numerous rules regarding anti-money laundering, know-your-customer requirements, and reporting obligations. AI systems can automatically monitor transactions, flag suspicious activities, and generate the necessary reports, reducing the burden on compliance staff and minimizing the risk of regulatory violations.
The back-office operations of banks have also benefited from AI implementation. Tasks such as document processing, data entry, and reconciliation that once required significant human labor can now be handled efficiently by AI systems. Optical character recognition technology combined with machine learning allows computers to read and process documents, extracting relevant information and updating databases automatically. This automation not only reduces costs but also minimizes human errors.
Despite these impressive capabilities, the integration of AI in banking is not without challenges. Banks must address concerns about data privacy, algorithmic bias, and the displacement of human workers. There are also questions about transparency – when an AI system makes a decision, customers and regulators may want to understand the reasoning behind it. Additionally, as banks become more dependent on AI systems, they must ensure these technologies are secure from cyber attacks and technical failures.
Ứng dụng trí tuệ nhân tạo trong ngành ngân hàng hiện đại với chatbot và phân tích dữ liệu
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C or D.
1. According to the passage, AI-powered chatbots differ from human operators because they:
A. provide more accurate information
B. can work without interruptions
C. speak multiple languages fluently
D. cost less to maintain
2. Modern AI fraud detection systems are superior to traditional systems because they:
A. follow predetermined rules more strictly
B. work faster than older computer systems
C. can learn and identify new fraudulent patterns
D. require less data to make decisions
3. The passage suggests that AI credit scoring:
A. has completely replaced traditional methods
B. only considers a person’s income level
C. analyzes more diverse information sources
D. is less accurate than conventional scoring
4. Robo-advisors are described as being most suitable for customers who:
A. have very large investment portfolios
B. need help with uncomplicated investments
C. want to avoid all investment risks
D. prefer not to use technology
5. In terms of regulatory compliance, AI systems help banks by:
A. eliminating the need for compliance staff
B. changing financial regulations automatically
C. automatically monitoring and reporting activities
D. reducing the number of rules banks must follow
Questions 6-9: True/False/Not Given
Do the following statements agree with the information in the passage? Write:
TRUE if the statement agrees with the information
FALSE if the statement contradicts the information
NOT GIVEN if there is no information on this
6. Banks worldwide began investing in AI technology more than ten years ago.
7. AI systems in banking can process customer inquiries from different time zones simultaneously.
8. Traditional credit scoring methods used more than five data points.
9. Robo-advisors are more expensive than human financial advisors for simple investment needs.
Questions 10-13: Sentence Completion
Complete the sentences below. Choose NO MORE THAN TWO WORDS from the passage for each answer.
10. AI technology helps with __ __ tasks like processing documents and entering data.
11. When combined with machine learning, __ __ __ allows computers to read documents automatically.
12. One concern about AI in banking is the potential __ of human employees.
13. Banks need to protect AI systems from __ __ and technical problems.
PASSAGE 2 – AI-Driven Risk Management and Market Analysis
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The financial services industry has embraced artificial intelligence as a transformative tool for risk management and market analysis, fundamentally altering how institutions assess and mitigate potential threats while capitalizing on market opportunities. This technological paradigm shift represents far more than simple automation; it constitutes a reconceptualization of how financial data is processed, interpreted, and leveraged for strategic decision-making.
A. Predictive Analytics in Risk Assessment
Traditional risk management methodologies relied heavily on historical data analysis and statistical models that assumed past patterns would reliably predict future outcomes. However, financial markets are inherently complex systems influenced by countless variables, including geopolitical events, consumer sentiment, technological disruptions, and macroeconomic trends. AI-powered predictive analytics transcends these limitations by employing neural networks and deep learning algorithms capable of processing vast datasets from disparate sources simultaneously. These systems can identify subtle correlations and emerging patterns that would be imperceptible to human analysts working with conventional tools.
For instance, hedge funds and investment banks now utilize AI systems that continuously monitor thousands of data streams, including news articles, social media posts, satellite imagery, and economic indicators. By applying natural language processing (NLP) to textual data and computer vision to visual information, these systems can gauge market sentiment and predict price movements with unprecedented accuracy. When political instability emerges in an oil-producing region, AI systems can immediately assess the ramifications for energy prices, currency valuations, and related securities, enabling traders to adjust positions before markets fully react.
B. Credit Risk Modeling
The sophistication of AI-driven credit risk assessment has evolved considerably beyond traditional FICO scores and basic underwriting criteria. Modern systems incorporate alternative data sources including utility payment histories, rental records, educational backgrounds, and even behavioral patterns observed through banking applications. Machine learning models can discern which combinations of factors most reliably correlate with loan repayment success, often revealing counterintuitive relationships that challenge conventional wisdom.
Several fintech companies have pioneered the use of AI for microfinance in developing economies, where traditional credit histories are often non-existent. By analyzing smartphone usage patterns, e-commerce transactions, and social network data, these systems can generate credit scores for individuals who would be considered “invisible” to traditional banking systems. This democratization of credit access has profound implications for economic development, though it also raises ethical questions about privacy and the appropriate use of personal data.
C. Market Manipulation Detection
Financial regulators face the daunting challenge of monitoring astronomical volumes of trading activity to identify market manipulation, insider trading, and other illicit practices. AI systems have become indispensable in this endeavor, capable of analyzing billions of trades to detect anomalous patterns that might indicate coordinated manipulation schemes. These systems employ graph analysis to map relationships between traders and temporal pattern recognition to identify suspicious timing of transactions.
One particularly sophisticated application involves detecting “spoofing” – a practice where traders place large orders they intend to cancel to create false impressions of supply or demand. AI algorithms can distinguish between legitimate trading strategies that involve order cancellations and malicious spoofing by analyzing the timing, frequency, and contextual factors surrounding these activities. As bad actors develop more elaborate schemes, AI systems continue adapting through continuous learning from new examples of manipulative behavior.
D. Algorithmic Trading and Portfolio Optimization
The ascendancy of algorithmic trading represents perhaps the most visible manifestation of AI’s influence in financial markets. High-frequency trading (HFT) firms utilize AI systems that execute thousands of trades per second, capitalizing on minuscule price discrepancies that exist for fractions of a second. These systems must make instantaneous decisions based on real-time market data, requiring computational capabilities far beyond human capacity.
Portfolio optimization has similarly been enhanced by AI, with systems capable of dynamically adjusting asset allocations in response to shifting market conditions. Unlike traditional rebalancing approaches that occur on fixed schedules, AI-driven systems continuously evaluate whether current allocations remain optimal given prevailing circumstances. They can incorporate factors such as correlation changes between asset classes, volatility regimes, and liquidity conditions to maintain portfolios that maximize risk-adjusted returns.
E. Challenges and Limitations
Despite their formidable capabilities, AI systems in finance are not infallible. The “black box” nature of some deep learning models creates transparency challenges – when an AI system denies a loan application or flags a transaction as suspicious, explaining the rationale behind that decision can be difficult. Regulators increasingly demand explainable AI that can provide clear justifications for automated decisions, particularly when those decisions affect consumers.
Additionally, AI systems can perpetuate and amplify biases present in their training data. If historical lending data reflects discriminatory practices, an AI system trained on that data may inadvertently continue those patterns. Financial institutions must vigilantly monitor their AI systems for such biases and implement corrective measures when they are detected. The concentration of AI adoption among large institutions also raises concerns about systemic risk – if many organizations rely on similar algorithms that react to market conditions in comparable ways, this homogeneity could exacerbate market volatility during stress periods.
Phân tích rủi ro tài chính và quản lý danh mục đầu tư bằng trí tuệ nhân tạo
Questions 14-26
Questions 14-18: Yes/No/Not Given
Do the following statements agree with the views of the writer in the passage? Write:
YES if the statement agrees with the views of the writer
NO if the statement contradicts the views of the writer
NOT GIVEN if it is impossible to say what the writer thinks about this
14. AI represents merely an improvement in automation rather than a fundamental change in financial data analysis.
15. Traditional risk management methods were inadequate because they assumed historical patterns would continue.
16. All hedge funds have achieved higher profits since implementing AI systems.
17. AI-driven credit assessment in developing economies raises ethical concerns about data privacy.
18. High-frequency trading firms make more profit than traditional investment companies.
Questions 19-23: Matching Headings
The passage has five sections, A-E. Choose the correct heading for each section from the list of headings below.
List of Headings:
i. The problem of algorithmic bias in financial services
ii. Using AI to catch illegal trading activities
iii. How AI predicts future financial risks better than old methods
iv. The complete replacement of human traders
v. Extending financial services to underserved populations
vi. Speed and precision in automated trading systems
vii. The future of banking without human employees
viii. Traditional versus modern credit evaluation approaches
19. Section A
20. Section B
21. Section C
22. Section D
23. Section E
Questions 24-26: Summary Completion
Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI systems used for detecting market manipulation employ various techniques including 24. __ __ to understand connections between different traders. One specific illegal practice called 25. __ involves placing orders that traders plan to cancel. Regulators now require 26. __ __ that can clearly justify their automated decisions.
PASSAGE 3 – The Epistemological and Structural Implications of AI Integration in Financial Ecosystems
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The proliferation of artificial intelligence throughout financial services infrastructure has precipitated a fundamental reconceptualization of epistemic frameworks through which market participants understand and navigate economic complexity. This transformation transcends mere technological augmentation, instead representing a paradigmatic shift in the ontological nature of financial decision-making itself. As AI systems increasingly mediate the relationship between data and action, they simultaneously reconfigure the structural architecture of markets while raising profound questions about agency, accountability, and the epistemological validity of algorithmically-derived insights.
The Transformation of Information Asymmetry
Classical financial theory posits that information asymmetry – the uneven distribution of knowledge between market participants – fundamentally shapes market dynamics and creates opportunities for arbitrage and value extraction. AI’s capacity to instantaneously process and analyze information from myriad sources has dramatically compressed these asymmetries, particularly regarding publicly available data. However, this compression has paradoxically created new forms of asymmetry centered not on access to raw information but on the sophistication of analytical capabilities and the proprietary nature of algorithms themselves.
Financial institutions that have developed advanced AI systems possess capabilities that far exceed those of smaller competitors or individual investors, creating a “capability divide” that may be more insurmountable than traditional information gaps. While public data becomes increasingly accessible to all market participants, the ability to extract actionable insights from that data becomes concentrated among organizations with substantial computational resources and specialized expertise. This dynamic raises normative questions about market equity and whether regulatory interventions might be necessary to mitigate the competitive advantages conferred by AI sophistication.
Furthermore, the opacity inherent in many AI models – particularly deep learning architectures with millions of parameters – creates a peculiar epistemic situation where even the institutions deploying these systems may not fully comprehend the causal mechanisms underlying their predictions. This “interpretability deficit” complicates risk assessment, as organizations cannot definitively determine whether their models are identifying genuine market patterns or merely spurious correlations that happen to align with historical data. The consequent reliance on statistical validation rather than mechanistic understanding represents a departure from traditional financial analysis, which emphasized causal explanations for market phenomena.
Systemic Risk and Algorithmic Homogeneity
The widespread adoption of similar AI methodologies across financial institutions introduces the possibility of correlated failures and synchronized behavior that could amplify systemic risk. When numerous market participants employ algorithms that respond to market conditions in comparable ways, the potential emerges for feedback loops that accelerate both upward and downward market movements. Academic research has documented several instances where algorithmic trading systems contributed to “flash crashes” – sudden, severe market dislocations that resolve within minutes or hours.
This phenomenon reflects a fundamental tension between individual rationality and collective stability. From the perspective of any single institution, adopting state-of-the-art AI systems represents a rational response to competitive pressures and fiduciary obligations to maximize returns. However, when such adoption becomes ubiquitous, the aggregate effect may be to increase market fragility rather than enhance efficiency. This coordination problem defies simple regulatory solutions, as mandating diversity in algorithmic approaches would be both practically infeasible and potentially detrimental to innovation.
The temporal dimension of AI decision-making further complicates this picture. High-frequency trading systems operate on timescales measured in milliseconds or microseconds, effectively creating a “high-speed layer” of market activity that exists largely independent of human oversight or intervention. During periods of market stress, these systems can execute thousands of trades before human operators can even assess what is occurring, let alone implement corrective measures. This temporal disconnect between algorithmic action and human comprehension represents an unprecedented challenge for market stability mechanisms designed in an era when trading occurred at human-comprehensible speeds.
Ethical Dimensions and Distributive Justice
The deployment of AI in financial services carries significant implications for distributive justice and economic equity. While proponents argue that AI-driven credit assessment can expand financial inclusion by identifying creditworthy individuals overlooked by traditional metrics, critics contend that these systems may perpetuate or exacerbate existing inequalities through algorithmic bias. The technical challenge of ensuring fairness in machine learning models is compounded by philosophical disagreements about how fairness should be conceptualized and operationalized in this context.
Different stakeholders prioritize distinct fairness criteria that may be mathematically incompatible. “Demographic parity” would require that different groups receive favorable decisions at equal rates, while “equalized odds” focuses on ensuring equal true positive and false positive rates across groups. “Predictive parity” emphasizes that predictions should be equally accurate for all groups. Rigorous mathematical analysis has demonstrated that simultaneously achieving all these fairness conceptions is generally impossible, necessitating explicit trade-offs between competing normative priorities.
Beyond statistical fairness, there are concerns about the use of proxy variables that may circumvent legal prohibitions against discrimination. An AI system that does not explicitly consider protected characteristics like race or gender might nevertheless learn to use correlated variables that effectively reproduce prohibited discrimination. The sophistication required to identify such implicit biases demands interdisciplinary collaboration between data scientists, domain experts, and ethicists – a level of coordination that remains relatively rare in industry practice.
The Evolution of Human-AI Collaboration
Rather than envisioning a future where AI completely supplants human judgment in financial services, a more nuanced perspective recognizes the emergent nature of hybrid decision-making systems that leverage the complementary strengths of both artificial and human intelligence. AI systems excel at processing vast quantities of structured data, identifying patterns, and maintaining consistency across numerous decisions. Human experts contribute contextual understanding, ethical reasoning, creativity in problem-solving, and the ability to navigate ambiguous situations that lack clear precedents.
The optimal configuration of these hybrid systems remains an active area of research and experimentation. Some organizations employ AI systems as decision support tools that provide recommendations which humans can accept, modify, or override. Others use AI for initial screening with human review reserved for borderline cases or situations where AI confidence is low. Still others are exploring “human-in-the-loop” architectures where humans and AI systems engage in iterative dialogue, refining decisions through successive interactions.
However, the efficacy of human oversight depends critically on maintaining genuine autonomy and critical engagement rather than perfunctory approval of algorithmic recommendations. Psychological research on “automation bias” demonstrates that humans tend to over-rely on automated systems, accepting their outputs even when those outputs contradict other available information. As AI systems become more sophisticated and generally reliable, this tendency toward excessive deference may intensify, potentially undermining the value of human oversight.
Looking forward, the continued integration of AI into financial services will require ongoing dialogue among technologists, regulators, industry practitioners, and civil society to navigate the multifaceted challenges this transformation presents. The trajectory of this evolution remains contingent on numerous factors, from technological breakthroughs in explainable AI to regulatory frameworks that appropriately balance innovation with stability and equity. What seems certain is that the financial landscape will continue to be profoundly shaped by the capabilities and limitations of artificial intelligence for the foreseeable future.
Tác động cấu trúc và triết học của AI đối với hệ sinh thái tài chính toàn cầu
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C or D.
27. According to the passage, the “capability divide” refers to:
A. different levels of access to financial information
B. gaps in computational power between organizations
C. variations in employee training across institutions
D. differences in algorithmic sophistication between market participants
28. The “interpretability deficit” mentioned in the passage means that:
A. AI systems make too many mistakes in predictions
B. organizations cannot fully understand how their AI models reach conclusions
C. financial data is too complex for any analysis
D. investors cannot access information about AI systems
29. Flash crashes are primarily caused by:
A. human traders making simultaneous errors
B. technological failures in trading platforms
C. algorithms responding to market conditions in similar ways
D. deliberate manipulation by large institutions
30. According to the passage, different fairness criteria in AI systems:
A. can all be achieved simultaneously with proper programming
B. are less important than accuracy in predictions
C. may be mathematically impossible to satisfy together
D. have been successfully implemented by most companies
31. The concept of “automation bias” suggests that humans:
A. prefer automated systems to human judgment
B. tend to accept AI recommendations too readily
C. cannot understand how AI systems work
D. should never override algorithmic decisions
Questions 32-36: Matching Features
Match each characteristic (32-36) with the correct aspect of AI in finance (A-F).
Characteristics:
32. Creates a new type of inequality based on analytical tools rather than data access
33. Operates at speeds that prevent real-time human intervention
34. May inadvertently use related variables to discriminate
35. Benefits from combining AI efficiency with human contextual understanding
36. Can intensify market movements through synchronized actions
Aspects:
A. Information asymmetry transformation
B. High-frequency trading systems
C. Algorithmic homogeneity
D. Proxy variable discrimination
E. Human-AI collaboration
F. Demographic parity
Questions 37-40: Short-answer Questions
Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What type of “architectures” make it difficult to understand AI decision-making processes?
38. What term describes the problem where individual rational actions harm collective outcomes in AI adoption?
39. What does research show humans tend to have excessive amounts of toward automated systems?
40. What type of AI development might help address the challenges of understanding algorithmic decisions?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- C
- B
- C
- FALSE
- TRUE
- NOT GIVEN
- FALSE
- back-office operations
- optical character recognition
- displacement
- cyber attacks
PASSAGE 2: Questions 14-26
- NO
- YES
- NOT GIVEN
- YES
- NOT GIVEN
- iii
- viii
- ii
- vi
- i
- graph analysis
- spoofing
- explainable AI
PASSAGE 3: Questions 27-40
- D
- B
- C
- C
- B
- A
- B
- D
- E
- C
- deep learning architectures
- coordination problem
- deference
- explainable AI
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI-powered chatbots, differ, human operators
- Vị trí trong bài: Đoạn 2, dòng 3-4
- Giải thích: Bài đọc nói rõ “Unlike human operators, these AI-powered assistants never need breaks, holidays, or sleep. They can operate continuously” – chatbot khác với nhân viên vì có thể hoạt động liên tục không cần nghỉ ngơi. Đây là paraphrase của “work without interruptions”.
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Modern AI fraud detection, superior, traditional systems
- Vị trí trong bài: Đoạn 3, dòng 2-6
- Giải thích: “Traditional fraud detection systems relied on rule-based approaches… However, modern AI systems use machine learning to analyze millions of transactions and identify suspicious patterns… These systems learn from every transaction, continuously improving” – hệ thống AI hiện đại học hỏi và nhận diện các mẫu gian lận mới, không chỉ dựa vào các quy tắc cũ.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI credit scoring
- Vị trí trong bài: Đoạn 4, dòng 3-5
- Giải thích: “AI systems can now analyze hundreds or even thousands of variables, including unconventional data sources like social media activity…” – AI phân tích nhiều nguồn thông tin đa dạng hơn, đây là paraphrase của “more diverse information sources”.
Câu 6: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Banks worldwide, investing, more than ten years ago
- Vị trí trong bài: Đoạn 1, dòng 1-2
- Giải thích: Bài viết nói “ways that were unimaginable just a decade ago” nghĩa là 10 năm trước điều này không thể tưởng tượng được, tức là các ngân hàng bắt đầu đầu tư TRONG vòng 10 năm gần đây, không phải HÔN HƠN 10 năm trước. Câu phát biểu mâu thuẫn với thông tin trong bài.
Câu 7: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI systems, process customer inquiries, different time zones, simultaneously
- Vị trí trong bài: Đoạn 2, dòng 1-2 và dòng 5-6
- Giải thích: “These automated systems can handle thousands of customer inquiries simultaneously” và “ensuring that customers receive support whenever they need it, regardless of time zones” – AI có thể xử lý nhiều yêu cầu cùng lúc bất kể múi giờ.
Câu 10: back-office operations
- Dạng câu hỏi: Sentence Completion
- Từ khóa: tasks, processing documents, entering data
- Vị trí trong bài: Đoạn 7, dòng 1-2
- Giải thích: “The back-office operations of banks have also benefited… Tasks such as document processing, data entry…” – cụm từ chính xác xuất hiện trong bài là “back-office operations”.
Câu 13: cyber attacks
- Dạng câu hỏi: Sentence Completion
- Từ khóa: protect AI systems, technical problems
- Vị trí trong bài: Đoạn 8, dòng cuối
- Giải thích: “they must ensure these technologies are secure from cyber attacks and technical failures” – ngân hàng cần bảo vệ hệ thống khỏi “cyber attacks” (tấn công mạng) và sự cố kỹ thuật.
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: AI, merely improvement, automation, fundamental change
- Vị trí trong bài: Đoạn 1, dòng 2-3
- Giải thích: Tác giả nói rõ “This technological paradigm shift represents far more than simple automation; it constitutes a reconceptualization” – đây là sự thay đổi cơ bản, không chỉ là cải tiến tự động hóa. Quan điểm tác giả trái ngược với câu phát biểu.
Câu 15: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Traditional risk management, inadequate, assumed historical patterns
- Vị trí trong bài: Section A, dòng 1-3
- Giải thích: “Traditional risk management methodologies relied heavily on historical data analysis and statistical models that assumed past patterns would reliably predict future outcomes. However, financial markets are inherently complex systems…” – tác giả chỉ ra hạn chế của phương pháp truyền thống là giả định quá khứ sẽ lặp lại.
Câu 17: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: AI-driven credit assessment, developing economies, ethical concerns, data privacy
- Vị trí trong bài: Section B, đoạn cuối
- Giải thích: “This democratization of credit access has profound implications for economic development, though it also raises ethical questions about privacy” – tác giả rõ ràng đề cập đến các câu hỏi đạo đức về quyền riêng tư.
Câu 19: iii (Section A)
- Dạng câu hỏi: Matching Headings
- Giải thích: Section A nói về “Predictive Analytics in Risk Assessment” – cách AI dự đoán rủi ro tương lai tốt hơn phương pháp cũ thông qua neural networks và deep learning algorithms. Heading iii “How AI predicts future financial risks better than old methods” phù hợp nhất.
Câu 20: viii (Section B)
- Dạng câu hỏi: Matching Headings
- Giải thích: Section B thảo luận về “Credit Risk Modeling” – so sánh phương pháp đánh giá tín dụng truyền thống (FICO scores) với hệ thống AI hiện đại (alternative data sources). Heading viii “Traditional versus modern credit evaluation approaches” phản ánh chính xác nội dung này.
Câu 24: graph analysis
- Dạng câu hỏi: Summary Completion
- Từ khóa: detecting market manipulation, connections between traders
- Vị trí trong bài: Section C, giữa đoạn
- Giải thích: “These systems employ graph analysis to map relationships between traders” – cụm từ chính xác là “graph analysis”.
Câu 26: explainable AI
- Dạng câu hỏi: Summary Completion
- Từ khóa: Regulators, clearly justify, automated decisions
- Vị trí trong bài: Section E, giữa đoạn
- Giải thích: “Regulators increasingly demand explainable AI that can provide clear justifications for automated decisions” – thuật ngữ chính xác là “explainable AI”.
Passage 3 – Giải Thích
Câu 27: D
- Dạng câu hỏi: Multiple Choice
- Từ khóa: capability divide
- Vị trí trong bài: Đoạn “The Transformation of Information Asymmetry”, đoạn 2
- Giải thích: “Financial institutions that have developed advanced AI systems possess capabilities that far exceed those of smaller competitors… creating a ‘capability divide'” – sự phân chia này liên quan đến độ tinh vi của thuật toán (algorithmic sophistication), không phải chỉ về sức mạnh tính toán hay đào tạo nhân viên.
Câu 28: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: interpretability deficit
- Vị trí trong bài: Đoạn “The Transformation of Information Asymmetry”, đoạn 3
- Giải thích: “the opacity inherent in many AI models… creates a peculiar epistemic situation where even the institutions deploying these systems may not fully comprehend the causal mechanisms underlying their predictions” – các tổ chức không thể hiểu đầy đủ cách thức AI đưa ra kết luận.
Câu 29: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: flash crashes, caused by
- Vị trí trong bài: Đoạn “Systemic Risk and Algorithmic Homogeneity”, đoạn 1
- Giải thích: “When numerous market participants employ algorithms that respond to market conditions in comparable ways, the potential emerges for feedback loops… Academic research has documented several instances where algorithmic trading systems contributed to ‘flash crashes'” – flash crashes xảy ra khi các thuật toán phản ứng theo cách tương tự.
Câu 30: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: different fairness criteria
- Vị trí trong bài: Đoạn “Ethical Dimensions and Distributive Justice”, đoạn 2
- Giải thích: “Rigorous mathematical analysis has demonstrated that simultaneously achieving all these fairness conceptions is generally impossible” – việc đạt được tất cả các tiêu chí công bằng cùng lúc thường là không thể về mặt toán học.
Câu 31: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: automation bias
- Vị trí trong bài: Đoạn “The Evolution of Human-AI Collaboration”, đoạn cuối
- Giải thích: “Psychological research on ‘automation bias’ demonstrates that humans tend to over-rely on automated systems, accepting their outputs even when those outputs contradict other available information” – con người có xu hướng chấp nhận kết quả của AI quá dễ dàng.
Câu 32: A
- Dạng câu hỏi: Matching Features
- Giải thích: Đặc điểm “creates a new type of inequality based on analytical tools” tương ứng với “Information asymmetry transformation” – đoạn này nói về việc bất bình đẳng mới dựa trên khả năng phân tích chứ không phải dữ liệu thô.
Câu 33: B
- Dạng câu hỏi: Matching Features
- Giải thích: “Operates at speeds that prevent real-time human intervention” khớp với “High-frequency trading systems” – hệ thống này hoạt động ở tốc độ milliseconds/microseconds, nhanh hơn khả năng can thiệp của con người.
Câu 37: deep learning architectures
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: architectures, difficult to understand, decision-making processes
- Vị trí trong bài: Đoạn “The Transformation of Information Asymmetry”, đoạn 3
- Giải thích: “the opacity inherent in many AI models – particularly deep learning architectures with millions of parameters” – đây là loại kiến trúc gây khó khăn cho việc hiểu quyết định của AI.
Câu 40: explainable AI
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: type of AI development, challenges, understanding algorithmic decisions
- Vị trí trong bài: Đoạn kết luận
- Giải thích: “from technological breakthroughs in explainable AI” – đây là loại AI có thể giải thích được, giúp giải quyết thách thức về tính minh bạch.
5. Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| transform | v | /trænsˈfɔːm/ | biến đổi, chuyển đổi hoàn toàn | AI is transforming the banking sector | digital transformation |
| algorithm | n | /ˈælɡərɪðəm/ | thuật toán | sophisticated algorithms that detect fraudulent transactions | machine learning algorithm |
| chatbot | n | /ˈtʃætbɒt/ | trợ lý ảo, bot trò chuyện | chatbots that answer customer queries 24/7 | AI-powered chatbot |
| fraud detection | n | /frɔːd dɪˈtekʃən/ | phát hiện gian lận | Fraud detection represents another critical area | fraud detection system |
| virtual assistant | n | /ˈvɜːtʃuəl əˈsɪstənt/ | trợ lý ảo | use of virtual assistants and chatbots | intelligent virtual assistant |
| machine learning | n | /məˈʃiːn ˈlɜːnɪŋ/ | học máy | modern AI systems use machine learning | machine learning model |
| credit scoring | n | /ˈkredɪt ˈskɔːrɪŋ/ | tính điểm tín dụng | Credit scoring has been revolutionized | credit scoring system |
| robo-advisor | n | /ˈrəʊbəʊ ədˈvaɪzə/ | cố vấn tự động | Robo-advisors use algorithms | robo-advisor platform |
| regulatory compliance | n | /ˈreɡjələtəri kəmˈplaɪəns/ | tuân thủ quy định | In the realm of regulatory compliance | regulatory compliance requirement |
| back-office operations | n | /bæk ˈɒfɪs ˌɒpəˈreɪʃənz/ | hoạt động hậu cần | back-office operations of banks | streamline back-office operations |
| optical character recognition | n | /ˈɒptɪkəl ˈkærəktə ˌrekəɡˈnɪʃən/ | nhận dạng ký tự quang học | Optical character recognition technology | OCR technology |
| algorithmic bias | n | /ˌælɡəˈrɪðmɪk ˈbaɪəs/ | thiên lệch thuật toán | concerns about algorithmic bias | address algorithmic bias |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | thay đổi mô hình tư duy | This technological paradigm shift | paradigm shift in thinking |
| predictive analytics | n | /prɪˈdɪktɪv ˌænəˈlɪtɪks/ | phân tích dự đoán | AI-powered predictive analytics | predictive analytics tools |
| neural network | n | /ˈnjʊərəl ˈnetwɜːk/ | mạng nơ-ron | employing neural networks | artificial neural network |
| natural language processing | n | /ˈnætʃrəl ˈlæŋɡwɪdʒ ˈprəʊsesɪŋ/ | xử lý ngôn ngữ tự nhiên | applying natural language processing | NLP techniques |
| hedge fund | n | /hedʒ fʌnd/ | quỹ đầu cơ | hedge funds and investment banks | hedge fund manager |
| underwriting | n | /ˈʌndəraɪtɪŋ/ | thẩm định bảo hiểm/tín dụng | basic underwriting criteria | underwriting process |
| fintech | n | /ˈfɪntek/ | công nghệ tài chính | Several fintech companies have pioneered | fintech innovation |
| microfinance | n | /ˈmaɪkrəʊfaɪnæns/ | tài chính vi mô | AI for microfinance | microfinance institution |
| market manipulation | n | /ˈmɑːkɪt məˌnɪpjuˈleɪʃən/ | thao túng thị trường | identify market manipulation | detect market manipulation |
| spoofing | n | /ˈspuːfɪŋ/ | giả mạo lệnh (giao dịch) | detecting “spoofing” | spoofing activity |
| high-frequency trading | n | /haɪ ˈfriːkwənsi ˈtreɪdɪŋ/ | giao dịch tần suất cao | High-frequency trading firms | HFT strategy |
| portfolio optimization | n | /pɔːtˈfəʊliəʊ ˌɒptɪmaɪˈzeɪʃən/ | tối ưu hóa danh mục đầu tư | Portfolio optimization has been enhanced | portfolio optimization technique |
| black box | n | /blæk bɒks/ | hộp đen (không minh bạch) | The “black box” nature of models | black box algorithm |
| explainable AI | n | /ɪkˈspleɪnəbəl eɪ aɪ/ | AI có thể giải thích được | Regulators demand explainable AI | explainable AI system |
| systemic risk | n | /sɪˈstemɪk rɪsk/ | rủi ro hệ thống | concerns about systemic risk | systemic risk assessment |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| proliferation | n | /prəˌlɪfəˈreɪʃən/ | sự phát triển nhanh chóng | The proliferation of AI | nuclear proliferation |
| epistemological | adj | /ɪˌpɪstɪməˈlɒdʒɪkəl/ | thuộc nhận thức luận | epistemological frameworks | epistemological approach |
| ontological | adj | /ˌɒntəˈlɒdʒɪkəl/ | thuộc bản thể luận | ontological nature of decision-making | ontological assumption |
| information asymmetry | n | /ˌɪnfəˈmeɪʃən əˈsɪmətri/ | bất cân xứng thông tin | information asymmetry fundamentally shapes | reduce information asymmetry |
| arbitrage | n | /ˈɑːbɪtrɑːʒ/ | kinh doanh chênh lệch giá | opportunities for arbitrage | arbitrage opportunity |
| capability divide | n | /ˌkeɪpəˈbɪləti dɪˈvaɪd/ | khoảng cách năng lực | creating a “capability divide” | bridge the capability divide |
| interpretability deficit | n | /ɪnˌtɜːprɪtəˈbɪləti ˈdefɪsɪt/ | thiếu hụt khả năng giải thích | This “interpretability deficit” complicates | address interpretability deficit |
| spurious correlation | n | /ˈspjʊəriəs ˌkɒrəˈleɪʃən/ | tương quan giả tạo | spurious correlations that happen to align | identify spurious correlation |
| flash crash | n | /flæʃ kræʃ/ | sụp đổ chớp nhoáng (thị trường) | contributed to “flash crashes” | flash crash event |
| fiduciary obligation | n | /fɪˈdjuːʃəri ˌɒblɪˈɡeɪʃən/ | nghĩa vụ ủy thác | fiduciary obligations to maximize returns | fiduciary obligation to clients |
| coordination problem | n | /kəʊˌɔːdɪˈneɪʃən ˈprɒbləm/ | vấn đề phối hợp | This coordination problem defies | solve coordination problem |
| distributive justice | n | /dɪˈstrɪbjʊtɪv ˈdʒʌstɪs/ | công bằng phân phối | implications for distributive justice | distributive justice theory |
| demographic parity | n | /ˌdeməˈɡræfɪk ˈpærəti/ | bình đẳng nhân khẩu học | “Demographic parity” would require | achieve demographic parity |
| proxy variable | n | /ˈprɒksi ˈveəriəbəl/ | biến đại diện | use of proxy variables | proxy variable analysis |
| automation bias | n | /ˌɔːtəˈmeɪʃən ˈbaɪəs/ | thiên lệch tự động hóa | research on “automation bias” | automation bias effect |
| human-in-the-loop | adj | /ˈhjuːmən ɪn ðə luːp/ | có sự tham gia của con người | “human-in-the-loop” architectures | human-in-the-loop system |
| interdisciplinary | adj | /ˌɪntədɪsɪˈplɪnəri/ | liên ngành | interdisciplinary collaboration | interdisciplinary approach |
Kết Bài
Chủ đề “How is artificial intelligence being used in financial services?” không chỉ phản ánh xu hướng công nghệ đương đại mà còn thể hiện sự phức tạp ngày càng tăng của bài thi IELTS Reading. Ba passages trong bộ đề này đã đưa bạn qua hành trình từ hiểu biết cơ bản về ứng dụng AI trong ngân hàng, đến phân tích sâu về quản lý rủi ro, và cuối cùng là những chiêm nghiệm triết học về tác động cấu trúc của AI đối với hệ sinh thái tài chính.
Với độ dài chuẩn quốc tế và 40 câu hỏi đa dạng, bộ đề này giúp bạn trải nghiệm như thi thật. Đáp án chi tiết kèm giải thích không chỉ cho bạn biết câu trả lời đúng mà còn hướng dẫn cách xác định thông tin, paraphrase từ khóa và áp dụng chiến lược làm bài hiệu quả. Bảng từ vựng phân loại theo passage giúp bạn xây dựng vốn từ chuyên ngành một cách có hệ thống.
Hãy luyện tập bộ đề này trong điều kiện thi thật – 60 phút không gián đoạn – để đánh giá chính xác năng lực hiện tại và xác định điểm cần cải thiện. Những chủ đề liên quan như How is the rise of fintech affecting traditional banking? và What are the challenges of regulating AI in the legal sector? cũng rất đáng tham khảo để mở rộng kiến thức và từ vựng. Việc hiểu rõ các vấn đề như What are the challenges of integrating renewable energy into national grids? sẽ giúp bạn làm quen với nhiều lĩnh vực học thuật khác nhau thường xuất hiện trong IELTS.
Đặc biệt, các chủ đề xã hội như Mental health awareness in educational institutions và Telemedicine for mental health services cũng thường xuyên xuất hiện trong đề thi, vì vậy việc làm quen với đa dạng chủ đề sẽ giúp bạn tự tin hơn khi bước vào phòng thi.
Chúc bạn đạt band điểm mục tiêu trong kỳ thi IELTS sắp tới!
